Study on potential common action and mechanism of Reduning Injection against SARS, MERS and COVID-19 based on network pharmacology and molecular docking technology.

Objective To explore the potential common mechanism and active ingredients of Reduning Injection against SARS, MERS and COVID-19 through network pharmacology and molecular docking technology. Methods The TCMSP database was used to retrieve the chemical components and targets of Artemisiae Annuae Herba, Lonicerae Japonicae Flos and Gardeniae Fructus in Reduning Injection. The gene corresponding to the target was searched by UniProt database, and Cytoscape 3.8.2 was used to build a medicinal material-compound-target (gene) network. Three coronavirus-related targets were collected in the Gene Cards database with the key words of "SARS""MERS" and "COVID-19", and common target of three coronavirus infection diseases were screened out through Venny 2.1.0 database. The common targets of SARS, MERS and COVID-19 were intersected with the targets of Reduning Injection, and the common targets were selected as research targets. Protein-protein interaction (PPI) network map were constructed by Cytoscape3.8.2 software after importing the common targets into the STRING database to obtain data. R language was used to carry out GO biological function enrichment analysis and KEGG signaling pathway enrichment analysis, histograms and bubble charts were drew, and component-target-pathway network diagrams was constructed. The key compounds in the component-target-pathway network were selected for molecular docking with important target proteins, novel coronavirus (SARS-CoV-2) 3CL hydrolase, and angiotensin-converting enzyme II (ACE2). Results 31 active compounds and 207 corresponding targets were obtained from Reduning Injection. 2 453 SARS-related targets, 805 MERS-related targets, 2 571 COVID-19-related targets, and 786 targets for the three diseases. 11 common targets with Reduning Injection: HSPA5, CRP, MAPK1, HMOX1, TGFB1, HSP90AA1, TP53, DPP4, CXCL10, PLAT, PRKACA. GO function enrichment analysis revealed 995 biological processes (BP), 71 molecular functions (MF), and 31 cellular components (CC). KEGG pathway enrichment analysis screened 99 signal pathways (P < 0.05), mainly related to prostate cancer, fluid shear stress and atherosclerosis, hepatocellular carcinoma, proteoglycans in cancer, lipid and atherosclerosis, human T-cell leukemia virus 1 infection, MAPK signaling pathway, etc. The molecular docking results showed that the three core active flavonoids of quercetin, luteolin, and kaempferol in Reduning Injection had good affinity with key targets MAPK1, PRKACA, and HSP90AA1, and the combination of the three active compounds with SARS-CoV-2 3CL hydrolase and ACE2 was less than the recommended chemical drugs. Conclusion Reduning Injection has potential common effects on the three diseases of SARS, MERS and COVID-19. This effect may be related to those active compounds such as quercetin, luteolin, and kaempferol acting on targets such as MAPK1, PRKACA, HSP90AA1 to regulate multiple signal pathways and exert anti-virus, suppression of inflammatory storm, and regulation of immune function.Copyright © 2022 Drug Evaluation Research. All rights reserved.

Artificial Intelligence Assisted Social Failure Mode and Effect Analysis (FMEA) for Sustainable Product Design

Nowadays, the social dimension of product sustainability is increasingly in demand, however, industrial designers struggle to pursue it much more than the environmental or economic one due to their unfamiliarity in correlating design choices with social impacts. In addition, this gap is not filled even by the supporting methods that have been conceived to only support specific areas of application. To fill this gap, this study proposed a method to support social failure mode and effect analysis (SFMEA), though the automatic failure determination, based on the use of a chatbot (i.e., an artificial intelligence (AI)-based chat). The method consists of 84 specific questions to ask the chatbot, resulting from the combination of known failures and social failures, elements from design theories, and syntactic structures. The starting hypothesis to be verified is that a GPT Chat (i.e., a common AI-based chat), properly queried, can provide all the main elements for the automatic compilation of a SFMEA (i.e., to determine the social failures). To do this, the proposed questions were tested in three case studies to extract all the failures and elements that express predefined SFMEA scenarios: a coffee cup provoking gender discrimination, a COVID mask denying a human right, and a thermometer undermining the cultural heritage of a community. The obtained results confirmed the starting hypothesis by showing the strengths and weaknesses of the obtained answers in relation to the following factors: the number and type of inputs (i.e., the failures) provided in the questions;the lexicon used in the question, favoring the use of technical terms derived from design theories and social sustainability taxonomies;the type of the problem. Through this test, the proposed method proved its ability to support the social sustainable design of different products and in different ways. However, a dutiful recommendation instead concerns the tool (i.e., the chatbot) due to its filters that limit some answers in which the designer tries to voluntarily hypothesize failures to explore their social consequences.

COVID – 19: A Critical Ontology of the present1

COVID-19 has crowned a number of other disasters (wildfires in Australia, Desert Locusts in Kenya, an imminent WWIII merging Iran and the US), causing panic to click into place and horror to become our global predicament, making us realize that we live in the illusion of the permanence of things, of mastery, and of immortality. People's turning to social media for trans-local news on COVID-19 has stirred great ire in the world. This led to the proliferation of dark images that associate the viral catastrophe with the end as we know it. To problematize the idea of the apocalypse (or the end) this paper speaks of three moments of survival in human existence: the beneath, the behind and the beyond. We argue that the apocalyptic nature of the pandemic and its global horrorism are part of a congeries of apocalyptic simulations that have always been part of the narrative with which we try to define ourexistence on earth. This paper masks itself against perfunctory examinations of the term apocalypse, and offers instead an understanding that runs along the lines of its Greek etymological sense as apokalyptein (revelation). It offers what Foucault calls an ontology of the present, that interrogates the history of COVID -19 with an emphasis neither on its origin nor on its telos. As beyondists, the COVID-19 catastrophe has revealed to us that 1) we have ‘access to knowledge beyond knowledge' (see Gumpert 2012), and therefore that 2) our modern predicament is not very modern. The end, (not) to be sure, has been lived and relived in the boundary between reality and simulation. After all, the end of something comprises the beginning (in reverse) of that which "endeth”, throwing the beyond, behind and beneath in the Ferris wheel of epistemological and existential entanglement.

Drug discovery for coronavirus disease 2019 based on text mining and biomedical databases.

Objective Based on text mining technology and biomedical database, data mining and analysis of coronavirus disease 2019 (COVID-19) were carried out, and COVID-19 and its main symptoms related to fever, cough and respiratory disorders were explored. Methods The common targets of COVID-19 and its main symptoms cough, fever and respiratory disorder were obtained by GenCLiP 3 website, Gene ontology in metascape database (GO) and pathway enrichment analysis, then STRING database and Cytoscape software were used to construct the protein interaction network of common targets, the core genes were screened and obtained. DGIdb database and Symmap database were used to predict the therapeutic drugs of traditional Chinese and Western medicine for the core genes. Results A total of 28 gene targets of COVID-19 and its main symptoms were obtained, including 16 core genes such as IL2, IL1B and CCL2. Through the screening of DGIdb database, 28 chemicals interacting with 16 key targets were obtained, including thalidomide, leflunomide and cyclosporine et al. And 70 kinds of Chinese meteria medica including Polygonum cuspidatum, Astragalus membranaceus and aloe. Conclusion The pathological mechanism of COVID-19 and its main symptoms may be related to 28 common genes such as CD4, KNG1 and VEGFA, which may participate in the pathological process of COVID-19 by mediating TNF, IL-17 and other signal pathways. Potentially effective drugs may play a role in the treatment of COVID-19 through action related target pathway.Copyright © 2022 Tianjin Press of Chinese Herbal Medicines. All Rights Reserved.

Internet of Things (IoT) in Smart Cities: A Statistical Survey

Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is a platform for collecting and storing the information that is accessed through various sensor-based IoT devices and make their information available in required and authorized domains. This interoperability can be achieved by semantic web technology. In this paper, I have reviewed multiple papers related to IoT in Smart Cities and presented a comparison among the semantic parameters. Moreover, I've presented my future domain of research which is about delivering the COVID-19 patients report to the concerned domains by the healthcare system domain. © 2023 IEEE.

Exploration of potential mechanism of SARS-CoV-2 induced immune inflammatory response leading to progression of atherosclerosis and prediction of traditional Chinese medicine for preventing and treating based on bioinformatics.

Objective To explore the core targets and important pathways of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) induced atherosclerosis (AS) progression from the perspective of immune inflammation, so as to predict the potential prevention and treatment of traditional Chinese medicine (TCM). Methods Microarray data were obtained from the Gene Expression Omnibus (GEO) database for coronavirus disease 2019 (COVID-19) patients and AS patients, and the "limmar" and "Venn" packages were used to screen out the common differentially expressed genes (DEGs) genes in both diseases. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses were performed on the common DEGs to annotate their functions and important pathways. The two gene sets were scored for immune cells and immune function to assess the level of immune cell infiltration. The protein-protein interaction (PPI) network was constructed by STRING database, and the CytoHubba plug-in of Cytoscape was used to identify the hub genes. Two external validation datasets were introduced to validate the hub genes and obtain the core genes. Immuno-infiltration analysis and gene set enrichment analysis (GSEA) were performed on the core genes respectively. Finally the potential TCM regulating the core genes were predicted by Coremine Medical database. Results A total of 7898 genes related to COVID-19, 471 genes related to AS progression;And 51 common DEGs, including 32 highly expressed genes and 19 low expressed genes were obtained. GO and KEGG analysis showed that common DEGs, which were mainly localized in cypermethrin-encapsulated vesicles, platelet alpha particles, phagocytic vesicle membranes and vesicles, were involved in many biological processes such as myeloid differentiation factor 88 (MyD88)-dependent Toll-like receptor signaling pathway transduction, interleukin-8 (IL-8) production and positive regulation, IL-6 production and positive regulation to play a role in regulating nicotinamide adenine dinucleotide phosphate oxidase activity, Toll-like receptor binding and lipopeptide and glycosaminoglycan binding through many biological pathways, including Toll-like receptor signaling pathways, neutrophil extracellular trap formation, complement and coagulation cascade reactions. The results of immune infiltration analysis demonstrated the state of immune microenvironment of COVID-19 and AS. A total of 5 hub genes were obtained after screening, among which Toll-like receptor 2 (TLR2), cluster of differentiation 163 (CD163) and complement C1q subcomponent subunit B (C1QB) genes passed external validation as core genes. The core genes showed strong correlation with immune process and inflammatory response in both immune infiltration analysis and GSEA enrichment analysis. A total of 35 TCMs, including Chuanxiong (Chuanxiong Rhizoma), Taoren (Persicae Semen), Danggui (Angelicae Sinensis Radix), Huangqin (Scutellariae Radix), Pugongying (Taraxaci Herba), Taizishen (Pseudostellariae Radix), Huangjing (Polygonati Rhizoma), could be used as potential therapeutic agents. Conclusion TLR2, CD163 and C1QB were the core molecules of SARS-CoV-2-mediated immune inflammatory response promoting AS progression, and targeting predicted herbs were potential drugs to slow down AS progression in COVID-19 patients.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

An Introduction to Biopolitics and Shock Economy of COVID-19: Medical Perspectives and Socioeconomic Dynamics

This edited volume on the biopolitics and shock economy of COVID-19 crisis embraces a wide spectrum of topics such as shock economy, medical perspectives on COVID-19, application of geospatial technology, infectivity, immunity, and severity of the disease, as well as ontology of the disease emergence as important factors for adoption of relevant biopolitical measures, sociocultural obstacles, COVID-19-induced transaction costs, social support and resilience of inhabitants of marginalized areas, as well as business resilience factors, entrepreneurship, and digital transformation. Through each chapter of this book, the authors, with their expertise in the theme they picked, have attempted to unfold some emerging aspects in the COVID-19 crisis which could benefit not only the academics but also the institutional, social, economic, developmental, and health policy-makers as well as the health practitioners on the ground. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Ontological Nature and Cause of COVID-19: A Philosophical Analysis

In this chapter, the ontological nature and cause of the coronavirus disease (COVID-19) is investigated. The outbreak of the novel COVID-19, coupled with the fact that a global pandemic occurs virtually every century, has brought to the fore the need to interrogate the ontological nature and cause of the COVID-19 pandemic. There have been different conspiracy theories flying all over the globe about COVID-19 since its outbreak in Wuhan city of China and subsequent global spread. One matter of considerable public concern about the theories is the uncorroborated claim that the new coronavirus (SARS-CoV-2) is manufactured in a laboratory at the Wuhan Institute of Virology as a biological weapon. This implies that the coronavirus is an artificial creation rather than a natural occurrence. Against this background, it is argued that the coronavirus is a natural phenomenon and that the resultant COVID-19, like other previous pandemics, is a privation of being. This chapter draws heavily on metaphysical works of Aristotle, Saint Augustine, and Thomas Aquinas to show that four types of cause, namely, material cause, formal cause, efficient cause, and final cause, are ontological components of every being in the natural world and that COVID-19 is not a being per se but rather a privation of being or good in a being. It is contended further that COVID-19 lacks a formal cause, and thus it cannot exist in isolation from a being (a human person or an animal) that has a formal cause. COVID-19 and other pandemics originally occur when a being is corrupted or its good nature is deprived of. It is concluded that to forestall further pandemic outbreak, humanity must stop upsetting and disrupting the natural order of things by desisting from eating certain animals and birds that are unfit for human consumption, or eating foods contaminated by such animals and birds. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A survey of epidemic management data models.

The COVID-19 epidemic has demonstrated the important role that data plays in the response to and management of public health emergencies. It has also heightened awareness of the role that ontologies play in the design of semantically precise data models that improve data interoperability among stakeholders. This paper surveys vocabularies and ontologies relevant to the task of achieving epidemic-related data interoperability. The paper first reviews 16 vocabularies and ontologies with respect to the use cases. Next it identifies patterns of knowledge that are common across multiple vocabularies and ontologies, followed by an analysis of patterns that are missing, based on the use cases. Conclusions show that existing vocabularies and ontologies provide significant coverage of the concepts underlying epidemic use cases, but there remain gaps in the coverage. More work is required to cover missing but significant concepts.

Effective medical center finding during COVID-19 pandemic using a spatial DSS centered on ontology engineering.

The global spread of the coronavirus has generated one of the most critical circumstances forcing healthcare systems to deal with it everywhere in the world. The complexity of crisis management, particularly in Iran, the unfamiliarity of the disease, and a lack of expertise, provided the foundation for researchers and implementers to propose innovative solutions. One of the most important obstacles in COVID-19 crisis management is the lack of information and the need for immediate and real-time data on the situation and appropriate solutions. Such complex problems fall into the category of semi-structured problems. In this respect, decision support systems use people's mental resources with computer capabilities to improve the quality of decisions. In synergetic situations, for instance, healthcare domains cooperating with spatial solutions, coming to a decision needs logical reasoning and high-level analysis. Therefore, it is necessary to add rich semantics to different classes of involved data, find their relationships, and conceptualize the knowledge domain. For the COVID-19 case in this study, ontologies allow for querying over such established relationships to find related medical solutions based on description logic. Bringing such capabilities to a spatial decision support system (SDSS) can help with better control of the COVID-19 pandemic. Ontology-based SDSS solution has been developed in this study due to the complexity of information related to coronavirus and its geospatial aspect in the city of Tehran. According to the results, ontology can rationalize different classes and properties about the user's clinical information, various medical centers, and users' priority. Then, based on the user's requests in a web-based SDSS, the system focuses on the inference made, advises the users on choosing the most related medical center, and navigates the user on a map. The ontology's capacity for reasoning, overcoming knowledge gaps, and combining geographic and descriptive criteria to choose a medical center all contributed to promising outcomes and the satisfaction of the sample community of evaluators.

Bioinformatics and system biology approach to identify the influences among COVID-19, ARDS and sepsis.

Background Severe coronavirus disease 2019 (COVID -19) has led to severe pneumonia or acute respiratory distress syndrome (ARDS) worldwide. we have noted that many critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. The molecular mechanisms that underlie COVID-19, ARDS and sepsis are not well understood. The objectives of this study were to analyze potential molecular mechanisms and identify potential drugs for the treatment of COVID-19, ARDS and sepsis using bioinformatics and a systems biology approach. Methods Three RNA-seq datasets (GSE171110, GSE76293 and GSE137342) from Gene Expression Omnibus (GEO) were employed to detect mutual differentially expressed genes (DEGs) for the patients with the COVID-19, ARDS and sepsis for functional enrichment, pathway analysis, and candidate drugs analysis. Results We obtained 110 common DEGs among COVID-19, ARDS and sepsis. ARG1, FCGR1A, MPO, and TLR5 are the most influential hub genes. The infection and immune-related pathways and functions are the main pathways and molecular functions of these three diseases. FOXC1, YY1, GATA2, FOXL, STAT1 and STAT3 are important TFs for COVID-19. mir-335-5p, miR-335-5p and hsa-mir-26a-5p were associated with COVID-19. Finally, the hub genes retrieved from the DSigDB database indicate multiple drug molecules and drug-targets interaction. Conclusion We performed a functional analysis under ontology terms and pathway analysis and found some common associations among COVID-19, ARDS and sepsis. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs were also identified on the datasets. We believe that the candidate drugs obtained in this study may contribute to the effective treatment of COVID-19.

Study on active compounds from Maxingganshi decoction for treatment of novel coronavirus pneumonia (COVID-19) based on network pharmacology and molecular docking method

Aim To explore the active compound of Maxingganshi decoction in treatment of novel coronavirus pneumonia(COVID-19). Methods With the help of TCMSP database, the chemical components and action targets of ephedra, almond, licorice, and gypsum in Maxingganshi decoction were searched, and then a C-T network, protein interaction analysis, GO functional enrichment analysis, and KEGG pathway enrichment were constructed. Analysis was performed to predict its mechanism of action. Results A total of 120 compounds in Maxingganshi decoction corresponded to 222 targets. PTGS2, ESR1, PPARG, AR, NOS2, NCOA2 acted on PI3K-Akt signaling pathway, TNF signaling pathway, IL-17 signaling pathway, T cell receptor signaling pathways, etc. The results of molecular docking showed that the affinity of quercetin, kaempferol, glabridin and other core compounds was similar to recommended drugs in treatment of COVID-19. Conclusions The active compounds of Maxingganshi decoction can target multiple pathways to achieve the therapeutic effect of COVID-19.Copyright © 2020 Publication Centre of Anhui Medical University. All rights reserved.

eCOVID-19: Development of ontology-based clinical decision support system for COVID-19

Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman. Material and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested. Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances. Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics. © 2022, Published by Frontiers in Health Informatics.

Questioning global health in the times of COVID-19: Re-imagining primary health care through the lens of politics of knowledge

The advent of the COVID-19 pandemic and the inequitable response to it has created a space for rethinking the knowledge translation that informs current health policy formulation and planning. Wide recognition of the failure of global health governance and national health systems has led to calls for reviving the Primary Health Care (PHC) agenda for post-COVID health systems development. Despite the joint international declaration on PHC made four decades ago, it has had limited application. This paper argues that the recent attempts to rethink PHC will prove inadequate without analysing and learning from the politics of knowledge (PoK) underlying global health policy and planning. Even with the growing relevance of the spirit of the Alma-Ata Declaration (1978) and its operationalisation as detailed in the report of conference proceedings, reassessment of reasons for its limited implementation continues to be located largely in the political economy of the medical establishment, the international economic order or in national governance flaws. Failure to address the dominant knowledge paradigm in the Alma Ata articulation of PHC has contributed to its limited application. This calls for expansion in the analysis from knowledge translation to generation and hierarchisation of knowledge. The paper discusses how the application of PoK as an analytical lens helps understand the power equations underlying the process of knowledge generation and its translation into policy and practice. Beneath the techno-centric and commodified health system is the dominant ‘knowledge' system whose foundations and assumptions ought to be interrogated. By following a PoK approach, a reorientation of thinking about the relationship between various forms of knowledge and knowledge holders is anticipated. A new health service system design is outlined—translating the spirit of PHC of 1978 into a ‘PHC Version 2.0'—that addresses the PoK gap in operational terms, with an approach to guide all levels of healthcare. It suggests how the world can be empowered to respond better by engaging with diverse ontologies and epistemologies to conceptualise knowledge and frame policies. Further, in the contexts of Asia, Africa and Latin America, it can contribute to the development of self-reliance to democratise general health policy and planning in the post-pandemic period.

RAMPVIS: A visualization and visual analytics infrastructure for COVID-19 data.

The COVID-19 pandemic generated large amounts of diverse data, including testing, treatments, vaccine trials, data from modeling, etc. To support epidemiologists and modeling scientists in their efforts to understand and respond to the pandemic, there arose a need for web visualization and visual analytics (VIS) applications to provide insights and support decision-making. In this paper, we present RAMPVIS, an infrastructure designed to support a range of observational, analytical, model-developmental, and dissemination tasks. One of the main features of the system is the ability to "propagate" a visualization designed for one data source to similar ones, this allows a user to quickly visualize large amounts of data. In addition to the COVID pandemic, the RAMPVIS software may be adapted and used with different data to provide rapid visualization support for other emergency responses.

Graph data science and machine learning for the detection of COVID-19 infection from symptoms.

Background: COVID-19 is an infectious disease caused by SARS-CoV-2. The symptoms of COVID-19 vary from mild-to-moderate respiratory illnesses, and it sometimes requires urgent medication. Therefore, it is crucial to detect COVID-19 at an early stage through specific clinical tests, testing kits, and medical devices. However, these tests are not always available during the time of the pandemic. Therefore, this study developed an automatic, intelligent, rapid, and real-time diagnostic model for the early detection of COVID-19 based on its symptoms. Methods: The COVID-19 knowledge graph (KG) constructed based on literature from heterogeneous data is imported to understand the COVID-19 different relations. We added human disease ontology to the COVID-19 KG and applied a node-embedding graph algorithm called fast random projection to extract an extra feature from the COVID-19 dataset. Subsequently, experiments were conducted using two machine learning (ML) pipelines to predict COVID-19 infection from its symptoms. Additionally, automatic tuning of the model hyperparameters was adopted. Results: We compared two graph-based ML models, logistic regression (LR) and random forest (RF) models. The proposed graph-based RF model achieved a small error rate = 0.0064 and the best scores on all performance metrics, including specificity = 98.71%, accuracy = 99.36%, precision = 99.65%, recall = 99.53%, and F1-score = 99.59%. Furthermore, the Matthews correlation coefficient achieved by the RF model was higher than that of the LR model. Comparative analysis with other ML algorithms and with studies from the literature showed that the proposed RF model exhibited the best detection accuracy. Conclusion: The graph-based RF model registered high performance in classifying the symptoms of COVID-19 infection, thereby indicating that the graph data science, in conjunction with ML techniques, helps improve performance and accelerate innovations.

COVID-19: An Ontological Human Circus?

Geopolitical and national interests predominate, given that a war between Russia and Ukraine would result in a daily economic decline in both countries. I am convinced that countries, not their people, wage wars. People have other concerns, such as food and a virus that has apparently not yet left these countries. Several plausibility arguments are presented in the first section of the paper, which addresses the persistently debated virus's origins. In contrast, its leadership continues to flounder. Numerous healthcare workers perished on the front lines, but there was scant coverage of their deaths during the first year of the pandemic and none since. The elderly, the frail elderly, and even the young are the most severely affected by the pandemic deaths that have occurred over the past two years and continue to occur. Current ontology is concerned with the controversies, hypotheses, and theories surrounding this damned insignificant RNA and its capacity to cause such catastrophic harm to humanity. Indeed, the issue is its disputed and contested origin. After two years, it appears that the graphs, countries, and news that are updated every minute on the Worldometer have not changed. However, something has changed;for example, countries have ceased to report the incidence of COVID-19 deaths. © 2023 Pulla. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

RESPONDING TO THE COVID-19 PANDEMIC WITH INTEGRAL AND MUTUAL HEALTHCARE ORIENTED TO THE WELL-BEING OF ALL PERSONS

Introduction. The complex problem of COVID-19 and the possibilities of responding through innovative general health strategies have been raised. The development of innovative health strategies was stimulated by the 1978 Alma Ata Declaration on Primary Health Care and by related perspectives around the 2018 Astana Conference. To this may be added the contributions of the International College and the Latin American Network of Person Centered Medicine toward the exploration and formulation of innovative concepts and strategies of health focused on persons.Objectives: (1) Ontological clarification of the concept of health and of the strategies and tactics for health actions;(2) Delineation of and assessment of multifactorial support for promising general health strategies focused on persons;and (3) Elucidation of the relevance of such strategies to respond to the demands of the COVID-19 pandemic.Methods: (a) Analysis and reflection on health concepts and strategies by a multidisciplinary authors group;(b) Selective review of the international and Latin American literature on health systems and the pandemic, displayed, and analyzed tabular and narratively.Results1. Ontological concepts corresponding to the meaning and functions of health and of specific general strategies and tactics to restore and promote health were formulated.2. A general health strategy was outlined based on an analysis of the process of building promising health constructs emerging from conferences and international declarations in recent years. Then, early and contemporary historical considerations, as well as contextual perspectives (Social Determinants of Health, the Sustainable Development Goals, and the Essential Functions of Public Health) were identified as supporting the specific elements of the persons-centered mutual and integral healthcare strategy.3. The relevance of the essential concept of health and the elements of the examined general health strategy was ascertained positively to respond to the demands of the current pandemic.Discussion: The value, implications, and limitations of the results obtained were discussed in the light of COVID-19 and future pandemics. Emerging recommendations on desirable next steps were also presented.Conclusions: The contributions of this study include the ontological clarification of the concept of person-centered health and the strategies and tactics for health actions, as well as the historical and contemporary multifactorial substantiation of the general health strategy involving Integral and Mutual Health Care Oriented to the Well-being of all Persons, particularly in the face of the demands of the COVID-19 pandemic.

Glioblastoma Susceptibility to SARS-CoV-2: Analysis of Cancer Stem Cells and Immune Tumor Microenvironment on Human Brain, Patient-Derived Tumors, and Organoid Models

INTRODUCTION: Glioblastoma (GBM) is the most common and deadliest primary brain tumor, characterized by chemoradiation resistance and an immunosuppressive tumor microenvironment (TME). SARS-CoV-2, the COVID-19 virus, produces a significant proinflammatory response and a spectrum of clinical presentations after central nervous system infection. METHOD(S): Patient-derived GBM tissue, primary cell lines, and organoids were analyzed with immunohistochemistry and pixel-line intensity quantification. Data from tumor-bulk and single-cell transcriptomics served to describe the cell-specific expression of SARS-CoV-2 receptors in GBM and its association with the immune TME phenotype. Normal brain and iPSC-derived organoids served as controls. RESULT(S): We demonstrate that patient-derivedGBMtissue and cell cultures express SARS-CoV2 entry factors such as ACE2, TMPRSS2, and NRP1. NRP1 expression was higher in GBM than in normal brains (p<0.05), where it plays a crucial role in SARS-CoV-2 infection. NRP1 was expressed in a cell-type and phenotype-specific manner and correlated with TME infiltration of immunosuppressive cells: M2 macrophages (r = 0.229), regulatory T cells (r = 0.459), NK cells (r = -0.346), and endothelial cells (r = 0.288) (p < 0.05). Furthermore, gene ontology enrichment analysis showed that leukocyte migration and chemotaxis are among the top 5 biological functions mediated by NRP1 (p < 0.05). We found our GBM organoids recapitulate tumoral expression of SARSCoV- 2 entry factors, which varies based on distance from surface as surrogate of TME oxygenation (p < 0.05). CONCLUSION(S): GBM cancer cells and immune TME cells express SARS-CoV-2 entry factors. Glioblastoma organoids recapitulate this expression and allow for currently undergoing studies analyzing the effect of SARS-CoV-2 infection in GBM. Our findings suggest that SARSCoV- 2 could potentially target GBM, opening the door to future studies evaluating SARS-CoV-2-driven immune modulation.

Big knowledge visualization of the COVID-19 CIDO ontology evolution.

BACKGROUND: The extensive international research for medications and vaccines for the devastating COVID-19 pandemic requires a standard reference ontology. Among the current COVID-19 ontologies, the Coronavirus Infectious Disease Ontology (CIDO) is the largest one. Furthermore, it keeps growing very frequently. Researchers using CIDO as a reference ontology, need a quick update about the content added in a recent release to know how relevant the new concepts are to their research needs. Although CIDO is only a medium size ontology, it is still a large knowledge base posing a challenge for a user interested in obtaining the "big picture" of content changes between releases. Both a theoretical framework and a proper visualization are required to provide such a "big picture". METHODS: The child-of-based layout of the weighted aggregate partial-area taxonomy summarization network (WAT) provides a "big picture" convenient visualization of the content of an ontology. In this paper we address the "big picture" of content changes between two releases of an ontology. We introduce a new DIFF framework named Diff Weighted Aggregate Taxonomy (DWAT) to display the differences between the WATs of two releases of an ontology. We use a layered approach which consists first of a DWAT of major subjects in CIDO, and then drill down a major subject of interest in the top-level DWAT to obtain a DWAT of secondary subjects and even further refined layers. RESULTS: A visualization of the Diff Weighted Aggregate Taxonomy is demonstrated on the CIDO ontology. The evolution of CIDO between 2020 and 2022 is demonstrated in two perspectives. Drilling down for a DWAT of secondary subject networks is also demonstrated. We illustrate how the DWAT of CIDO provides insight into its evolution. CONCLUSIONS: The new Diff Weighted Aggregate Taxonomy enables a layered approach to view the "big picture" of the changes in the content between two releases of an ontology.